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source("tianfengRwrappers.R")

载入需要的程辑包：dplyr

载入程辑包：‘dplyr’

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    count

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    combine

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    intersect, setdiff, union

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    intersect

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    collapse, desc, intersect, setdiff, slice, union

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载入需要的程辑包：reticulate
载入需要的程辑包：tidyr

载入程辑包：‘tidyr’

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载入程辑包：‘MySeuratWrappers’

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    DimPlot, DoHeatmap, LabelClusters, RidgePlot, VlnPlot


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载入程辑包：‘reshape2’

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NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.

载入程辑包：‘clusterProfiler’

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Registering fonts with R

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载入程辑包：‘DT’

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========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

载入需要的程辑包：grid
========================================
ComplexHeatmap version 2.2.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================


载入程辑包：‘ComplexHeatmap’

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    add_heatmap

CCA method to integrate stromal cells in carotid and coronary artery plaques

#提取SMC细胞亚群
SMCs_list <- list(ds0,ds2)

SMCs_list <- lapply(X = SMCs_list, FUN = function(x) {
    x <- NormalizeData(x)
    x <- FindVariableFeatures(x, selSMCtion.method = "vst", nfeatures = 2000)
})
#需要分析的差异基因
int_features <- SelectIntegrationFeatures(object.list = SMCs_list)
#选择合并的anchor特征
int_anchors <- FindIntegrationAnchors(object.list = SMCs_list, anchor.features = int_features)

#根据anchor合并
SMCs_combined <- IntegrateData(anchorset = int_anchors)

DefaultAssay(SMCs_combined) <- "integrated"
rm("SMCs_list","int_features","int_anchors")

SMCs_combined <- ScaleData(SMCs_combined, verbose = FALSE)
SMCs_combined <- RunPCA(SMCs_combined, npcs = 30, verbose = FALSE)
SMCs_combined <- RunUMAP(SMCs_combined, reduction = "pca", dims = 1:30)

SMCs_combined <- FindNeighbors(SMCs_combined, reduction = "pca", dims = 1:30)
SMCs_combined <- FindClusters(SMCs_combined, resolution = 0.1) # resolution 取0.1 或 0.2
umapplot(SMCs_combined)

SMCs_combined <- FindClusters(SMCs_combined, resolution = 0.2) # resolution 取0.1 或 0.2
umapplot(SMCs_combined)

umapplot(SMCs_combined, split.by = "conditions")
# Idents(SMCs_combined) <- SMCs_combined$orig.ident 
# # SMCs_combined <- RenameIdents(SMCs_combined,
#                                '1'='coronary arteries','2'='coronary arteries',
#                               '3'='coronary arteries','4'='coronary arteries',
#                               '5'='coronary arteries','6'='coronary arteries',
#                               '7'='coronary arteries','8'='coronary arteries',
#                               'CA_sample1.txt'='carotid arteries',
#                               'CA_sample2.txt'='carotid arteries','CA_sample3.txt'='carotid arteries',)
# SMCs_combined$conditions <- Idents(SMCs_combined)

# Idents(SMCs_combined) <- SMCs_combined$conditions
# ds0_SMC <- merge(subset(SMCs_combined,ident = "NA"),subset(SMCs_combined,ident = "AC"))
# ds0_SMC@reductions[["umap"]] <- SMCs_combined@reductions[["umap"]]

multi_featureplot(c("DCN","LUM","MMP2","ACTA2"),SMCs_combined,labels = "",label = F,min.cutoff = 0)

negative values

datamat[datamat>0] <- 0
datamat[datamat<0] <- 1


pheatmap::pheatmap(datamat, color = c("#FFFFFF", "#000000"),
        border_color = NA, cluster_rows = T, cluster_cols = FALSE,
        main = "CCA data", show_rownames = F,show_colnames = F)

fig.3

CCA as a unsupervised method to integrate stromal cells in all datasets

CAD_merge_CCA <- FindClusters(CAD_merge_CCA, resolution = 0.2) # resolution 取0.1 或 0.2

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 19199
Number of edges: 750750

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.9332
Number of communities: 10
Elapsed time: 3 seconds

umapplot(CAD_merge_CCA)

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